Apparatus and method for compressing continuous data

ABSTRACT

Disclosed are an apparatus and method for compressing continuous data. The apparatus for compressing continuous data may include a data generator configured to calculate differences between adjacent values in original continuous data and generate data based on the calculated differences.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Korean Patent Application No.10-2015-0163600, filed on Nov. 20, 2015, in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein in itsentirety by reference.

BACKGROUND

1. Field

Apparatuses and methods consistent with exemplary embodiments relate todata compression technology, and more particularly to an apparatus andmethod for compressing continuous data.

2. Description of Related Art

Diabetes mellitus is a chronic disease that causes various complicationsand cannot be easily treated. Thus, the complications should beprevented by regularly checking blood sugar levels. In addition, theblood sugar levels should be checked in order to prepare for low bloodsugar and adjust an insulin dose when insulin is injected.

A non-invasive blood sugar measuring method using a near-infraredspectrometer has recently been developed as a blood sugar measuringmethod. A glucose meter using a near-infrared spectrometer, which isdeveloped to continuously monitor blood sugar levels, predicts bloodsugar levels from absorbance data obtained by applying near-infraredrays to skin at a certain time interval.

The absorbance data that is acquired to continuously monitor the bloodsugar levels has a vast volume, and thus is necessarily required to beefficiently stored and managed. In particular, the absorbance data isexpressed as a value that varies continuously with a change inwavelength, and thus there is a need for a method for efficientlystoring and managing such data.

SUMMARY

Exemplary embodiments overcome the above problems and/or disadvantagesand other disadvantages not described above. Also, the exemplaryembodiments are not required to overcome the disadvantages describedabove, and may not overcome any of the problems described above

One or more exemplary embodiments may provide an apparatus and methodfor compressing continuous data.

Additional exemplary aspects will be set forth in part in thedescription which follows and, in part, will be apparent from thedescription, or may be learned by practice of the presented embodiments.

According to an aspect of an exemplary embodiment, there is provided anapparatus for compressing continuous data, the apparatus including: adata generator configured to calculate differences between adjacentvalues in original continuous data and generate data based on thecalculated differences.

The original continuous data may be one of Photoplethysmograph (PPG)data and absorbance data.

The generated data may include a first value of the original continuousdata and the calculated differences.

The apparatus may further include a sign remover configured to compareeach of a plurality of values of the generated data with its precedingvalue to detect a value whose sign is changed, show the sign of thedetected value, and remove signs of remaining values

The sign remover may show a sign of a first value of the generated data.

The apparatus may further include a decimal point remover configured toremove a decimal point from each of the values of the generated data ifthe new data comprises values with an identical number of decimalplaces.

The decimal point remover may remove a zero before a significant digitfrom each of the values of the generated data.

The apparatus may further include a second compressor configured tocompress the generated data from which the signs of the remaining valuesare removed using a lossless compression algorithm or a lossycompression algorithm.

The lossless compression algorithm may include a run-length encoding(RLE) algorithm, a dictionary-based encoding algorithm, and a Huffmancoding algorithm.

According to an aspect of another exemplary embodiment, there isprovided a method of compressing continuous data, the method including:calculating differences between adjacent values in original continuousdata; and generating data based on the calculated differences.

The original continuous data may be one of PPG data and absorbance data.

The generated data may include a first value of the original continuousdata and the calculated differences.

The method may further include comparing each of a plurality of valuesof the generated data with its preceding value to detect a value whosesign is changed, showing the sign of the detected value, and removingsigns of remaining values

The method may further include showing a sign of a first value of thegenerated data.

The method may further include removing a decimal point from each of thevalues of the generated data if the generated data comprises values withan identical number of decimal places.

The method may further include removing a zero before a significantfeature from each of the values of the generated data.

The method may further include compressing the generated data from whichthe signs of the remaining values are removed using a losslesscompression algorithm or a lossy compression algorithm.

The lossless compression algorithm may include an RLE algorithm, adictionary-based encoding algorithm, and a Huffman coding algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view showing original continuous data according to anexemplary embodiment.

FIG. 2 is a block diagram showing a compression and decompression systemaccording to an exemplary embodiment.

FIG. 3 is a block diagram showing a compression device according to anexemplary embodiment.

FIG. 4 is a block diagram showing a first compression unit according toan exemplary embodiment.

FIG. 5 is a block diagram showing a first compression unit according toanother exemplary embodiment.

FIG. 6 is a flowchart showing a method of compressing continuous dataaccording to an exemplary embodiment.

FIG. 7 is a flowchart showing a method of reducing the amount oforiginal continuous data according to an exemplary embodiment.

DETAILED DESCRIPTION

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The relative size anddepiction of these elements may be exaggerated for clarity,illustration, and convenience. The matters defined in the description,such as detailed construction and elements, are provided to assist in acomprehensive understanding of the exemplary embodiments. However, it isapparent that the exemplary embodiments can be practiced without thosespecifically defined matters. Also, well-known functions orconstructions are not described in detail since they would obscure thedescription with unnecessary detail.

The following description is provided to assist the reader in gaining acomprehensive understanding of the methods, apparatuses, and/or systemsdescribed herein. Accordingly, various changes, modifications, andequivalents of the methods, apparatuses, and/or systems described hereinwill be suggested to those of ordinary skill in the art. Also,descriptions of well-known functions and constructions may be omittedfor increased clarity and conciseness.

Hereinafter, exemplary embodiments will be described in detail withreference to the accompanying drawings. The terms used herein aredefined in consideration of the functions of the exemplary embodimentsand may be changed depending on a user, the intent of an operator, or acustom. Accordingly, the terms should be defined based on the followingoverall description of this specification.

Continuous data to be compressed (hereinafter referred to as originalcontinuous data) may be data that is expressed as a value that variescontinuously but only slightly. For example, the original continuousdata may be biometric data including photoplethysmograph (PPG) data,absorbance data, etc., but is not limited thereto.

FIG. 1 is a view showing original continuous data according to anexemplary embodiment. The original continuous data of FIG. 1 showsabsorbance data 110 measured by applying light to a sample and detectinglight scattered or reflected from the sample.

Referring to FIG. 1, the absorbance data 110 may be expressed as onevalue for each wavelength, and the absorbance data 110 measured from thesame sample may have the following characteristics.

First, the absorbance data 110 may be expressed as a value that variescontinuously but only slightly. That is, the absorbance data 110 may bechanged gradually.

Second, the absorbance data 110 may be similar even when the absorbancedata 110 is measured at a different time in regards to the same sample.

Third, the absorbance data 110 may be expressed as values with the samenumber of decimal places.

Fourth, data at a specific wavelength may be important in analyzing theabsorbance data 110. When the concentration of a specific materialchanges, there is a wavelength at which an absorbance value changes.Accordingly, a value at a corresponding wavelength 120 may be importantin analyzing the absorbance data 110.

FIG. 2 is a block diagram showing a compression and decompression systemaccording to an exemplary embodiment.

Referring to FIG. 2, a compression and decompression system 200according to an exemplary embodiment may include a compression device210, a storage device 220, and a decompression device 230.

The compression device 210 may reduce the amount of original continuousdata, and apply various compression algorithms to compress the originalcontinuous data. Here, the compression algorithms may include losslesscompression algorithms that may compress data without any loss and lossycompression algorithms that may improve compression rates but lose aportion of the data. The lossless compression algorithms may include arun-length encoding (RLE) algorithm, a dictionary-based encodingalgorithm (e.g., a Lempel-Ziv algorithm), a Huffman coding algorithm,etc., and the lossy compression algorithms may include a moving pictureexperts group (MPEG) algorithm, a joint photographic experts group(JPEG) algorithm, etc., but are not limited thereto.

The compression device 210 will be described later in detail withreference to FIG. 3.

The storage device 220 may store data compressed by the compressiondevice 210. The storage device 220 may include at least one storagemedium among a flash memory type memory, a hard disk type memory, amultimedia card micro type memory, a card type memory (e.g., securedigital (SD) or eXtreme digital (XD) memory), a random access memory(RAM), a static random access memory (SRAM), a read-only memory (ROM),an electrically erasable programmable read-only memory (EEPROM), aprogrammable read-only memory (PROM), a magnetic memory, a magneticdisk, and an optical disc.

The decompression device 230 may apply a decompression algorithmcorresponding to the compression algorithm applied to the compressiondevice 210 to restore the original continuous data.

FIG. 3 is a block diagram showing the compression device 210 accordingto an exemplary embodiment.

Referring to FIG. 3, the compression device 210 according to anexemplary embodiment may include a first compressor 310 and a secondcompressor 320.

The first compressor 310 may reduce the amount of original continuousdata to generate new data.

According to an exemplary embodiment, the first compressor 310 mayselect, from the original continuous data, values of meaningful partsthat are important in analyzing the original continuous data andgenerate new data composed of the selected values.

As described above, for absorbance data used to measure blood sugarlevels, absorbance values at a specific wavelength range are important.That is, because the change in concentration of glucose affectsabsorbance in a specific wavelength range, it is possible to reduce theamount of original continuous data by generating new data composed ofthe absorbance values at the specific wavelength range.

TABLE 1 ORIGINAL 123456 1.12345 1.01234 0.98765 0.87654 0.89012CONTINUOUS 0.90123 0.91234 0.90012 0.89999 DATA NEW 1.12345 1.012340.98765 0.87654 0.89012 DATA

Referring to Table 1, for example, the first compressor 310 may selectmeaningful parts that are important in analyzing the original continuousdata, that is, second to sixth values 1.12345, 1.01234, 0.98765,0.87654, and 0.89012, from the original continuous data, and generatenew data composed of the selected second to sixth values 1.12345,1.01234, 0.98765, 0.87654, and 0.89012.

According to another exemplary embodiment, the first compressor 310 mayround off values of the original continuous data to a predeterminednumber of decimal places and generate new data having a smaller amountthan the original continuous data on the basis of a result of therounding-off.

TABLE 2 ORIGINAL 1.23456 1.12345 1.01234 0.98765 0.87654 0.89012CONTINUOUS 0.90123 0.91234 0.90012 0.89999 DATA NEW 1.235 1.123 1.0120.988 0.877 0.890 0.901 0.912 DATA 0.900 0.900

Referring to Table 2, for example, if the values of the originalcontinuous data have five decimal places, the first compressor 310 mayround off the values of the original continuous data to three decimalplaces and generate new data composed of values having three decimalplaces 1.235, 1.123, 1.012, 0.988, 0.877, 0.890, 0.901, 0.912, 0.900,and 0.900.

According to another exemplary embodiment, the first compressor 310 mayselect some values of the original continuous data using amulti-variable analysis technique such as Principal Component Analysis(PCA) and generate new data composed of the selected values.

According to another exemplary embodiment, the first compressor 310 mayselect values from the original continuous data at a predetermined datainterval and generate new data composed of the selected values.

As described above, because the absorbance data changes gradually with achange in wavelength, an absorbance value at a specific wavelength issimilar to an average of its left and right values. Accordingly, if theoriginal continuous data changes gradually like the absorbance data, itis possible to reduce the amount of data by selecting values from theoriginal continuous data at a certain data interval (e.g., 2) andpredicting non-selected values on the basis of the selected values.

In this case, the non-selected values may be predicted using apredefined prediction equation on the basis of values of new data. Theprediction equation may be defined as an average of multiple adjacentvalues and may also be defined as a function for the multiple adjacentvalues (e.g., a polynomial).

TABLE 3 ORIGINAL 1.23456 1.12345 1.01234 0.98765 0.87654 0.89012CONTINUOUS 0.90123 0.91234 0.90012 0.89999 DATA NEW 1.23456 1.012340.87654 0.90123 0.90012 0.89999 DATA PREDICTED 1.12345 0.94114 0.888850.90068

Referring to Table 3, for example, if the data interval is 2, the firstcompressor 310 may select a first value 1.23456, a third value 1.01234,a fifth value 0.87654, a seventh value 0.90123, and a ninth value0.90012 from the original continuous data and generate new data composedof the selected values. In this case, a tenth value 0.89999 of theoriginal continuous data which is the last value of the originalcontinuous data is difficult to predict upon decompression. Accordingly,the first compressor 310 may generate new data including the last value,that is, the tenth value 0.89999.

The second value that is not selected from the original continuous datamay be predicted as an average 1.12345 of a first value 1.23456 and asecond value 1.01234 of the new data. The fourth value that is notselected from the original continuous data may be predicted as anaverage 0.94444 of the second value 1.01234 and a third value 0.87654 ofthe new data. The sixth value that is not selected from the originalcontinuous data may be predicted as an average 0.88885 of the thirdvalue 0.87654 and a fourth value 0.90123 of the new data. An eighthvalue that is not selected from the original continuous data may bepredicted as an average 0.90068 (rounded off to six decimal places) ofthe fourth value 0.90123 and a fifth value 0.90012 of the new data.

If the method of reducing the amount of data on the basis of valuesselected at a predetermined data interval is used, the first compressor310 may find a difference between a predicted value and an originalvalue and generate prediction difference data on the basis of thedifference. The prediction difference data may be used to restore theoriginal continuous data without any loss.

TABLE 4 ORIGINAL 1.23456 1.12345 1.01234 0.98765 0.87654 0.89012CONTINUOUS 0.90123 0.91234 0.90012 0.89999 DATA NEW 1.23456 1.012340.87654 0.90123 0.90012 0.89999 DATA PREDICTED 1.12345 0.94444 0.888850.90068 PREDICTION 0 −0.04321 −0.00127 −0.01166 DIFFERENCE DATA

Referring to Table 4, for example, if the data interval is 2, the firstcompressor 310 may select the first value 1.23456, the third value1.01234, the fifth value 0.87654, the seventh value 0.90123, and theninth value 0.90012 from the original continuous data and generate newdata composed of the selected values and the last value 0.89999.

The first compressor 310 may calculate a difference zero (0) between thenon-selected second value 1.12345 and its predicted value 1.12345,calculate a difference −0.04321 between the non-selected fourth value0.98765 and its predicted value 0.94444, calculate a difference −0.00127between the non-selected sixth value 0.89012 and its predicted value0.88885, calculate a difference −0.01166 between the non-selected eighthvalue 0.91234 and its predicted value 0.90068, and generate predictiondifference data composed of the differences 0, −0.04321, −0.00127, and−0.01166.

According to another exemplary embodiment, if the values of the originalcontinuous data have the same number of decimal places, the firstcompressor 310 may convert the values of the original continuous datainto integers and generate new data having a smaller amount than theoriginal continuous data on the basis of a result of the conversion.

As described above, the absorbance data may be expressed as values withthe same number of decimal places. Accordingly, if the originalcontinuous data is expressed as values with the same number of decimalplaces like the absorbance data, it is possible to reduce the amount oforiginal continuous data by deleting a decimal point and a zero before asignificant digit from each of the values of the original continuousdata to convert the values into integers.

TABLE 5 ORIGINAL 1.23456 1.12345 1.01234 0.98765 0.87654 0.89012CONTINUOUS 0.90123 0.91234 0.90012 0.89999 DATA NEW 123456 112345 10123498765 87654 89012 90123 DATA 91234 90012 89999

Referring to Table 5, for example, the first compressor 310 may delete adecimal point and a zero before a significant digit from each of thevalues 1.23456, 1.12345, 1.01234, 0.98765, 0.87654, 0.89012, 0.90123,0.91234, 0.90012, and 0.89999 of the original continuous data andgenerate new data composed of new values 123456, 112345, 101234, 98765,87654, 89012, 90123, 91234, 90012, and 89999 on the basis of a result ofthe deletion.

According to another exemplary embodiment, the first compressor 310 maycalculate differences between adjacent values of the original continuousdata and generate new data having a smaller amount than the originalcontinuous data on the basis of the calculated differences.

As described above, because the absorbance data changes gradually withthe change in wavelength, an absorbance value at a specific wavelengthhas no significant difference from that at its preceding wavelength.Accordingly, if the original continuous data changes gradually like theabsorbance data, it is possible to reduce the amount of data bygenerating new data on the basis of the difference between the adjacentvalues of the original continuous data.

TABLE 6 ORIGINAL 123456 112345 101234 98765 87654 89012 90123 CONTINUOUS91234 90012 89999 DATA NEW 123456 −11111 −11111 −2469 −11111 1358 1111DATA 1111 −1222 −13

Referring to Table 6, for example, the first compressor 310 maycalculate differences between adjacent values 123456 and 112345, 112345and 101234, 101234 and 98765, 98765 and 87654, 87654 and 89012, 89012and 90123, 90123 and 91234, 91234 and 90012, and 90012 and 89999 of theoriginal continuous data, and generate new data composed of the firstvalue 123456 and the calculated differences −11111, −11111, −2469,−11111, 1358, 1111, 1111, −1222, and −13.

If the method of reducing the amount of data on the basis of thedifferences between the adjacent values is used, the first compressor310 may find a difference between the differences to further reduce theamount of data.

TABLE 7 ORIGINAL 123456 112345 101234 98765 87654 89012 90123 91234CONTNUOUS 90012 89999 DATA FIRST 123456 −11111 −11111 −2469 −11111 13581111 1111 NEW −1222 −13 DATA SECOND 123456 −11111 0 8642 −8642 12469−247 0 −2333 1209 NEW DATA

Referring to Table 7, for example, the first compressor 310 maycalculate differences between adjacent values 123456 and 112345, 112345and 101234, 101234 and 98765, 98765 and 87654, 87654 and 89012, 89012and 90123, 90123 and 91234, 91234 and 90012, and 90012 and 89999 of theoriginal continuous data, and generate first new data composed of thefirst value 123456 and the calculated differences −11111, −11111, −2469,−11111, 1358, 1111, 1111, −1222, and −13.

The first compressor 310 may calculate differences between adjacentvalues −11111 and −11111, −11111 and −2469, −2469 and −11111, −11111 and1358, 1358 and 1111, 1111 and 1111, 1111 and −1222, and −1222 and −13 ofthe generated first new data, and generate second new data composed ofthe first value 123456 and second value −11111 of the first new data andthe calculated differences 0, 8642, −8642, 12469, −247, 0, −2333, and1209.

According to another exemplary embodiment, the first compressor 310 mayreduce the amount of data by generating new data on the basis of thedifferences between the adjacent values of the original continuous data,comparing each of a plurality of values of the generated new data withits preceding value to detect a value whose sign is changed, showing thesign of the detected value, and removing signs of the remaining values.

As described above, because the absorbance data changes gradually withthe change in wavelength, an absorbance value at a specific wavelengthhas no significant difference from that at its preceding wavelength.Accordingly, if the original continuous data changes gradually like theabsorbance data, the differences between the adjacent values haveconsecutive negative signs (−) or positive signs (+). Accordingly, ifthere are consecutive negative signs or positive signs, it is possibleto further reduce the amount of data by showing only the first sign ofthe consecutive signs.

TABLE 8 ORIGINAL 123456 112345 101234 98765 87654 89012 90123 CONTINUOUS91234 90012 89999 DATA FIRST 123456 −11111 −11111 −2469 −11111 1358 11111111 NEW −1222 −13 DATA SECOND +123456 −11111 11111 2469 11111 +13581111 1111 NEW −1222 13 DATA

Referring to Table 8, for example, the first compressor 310 maycalculate differences between adjacent values 123456 and 112345, 112345and 101234, 101234 and 98765, 98765 and 87654, 87654 and 89012, 89012and 90123, 90123 and 91234, 91234 and 90012, and 90012 and 89999 of theoriginal continuous data, and generate first new data composed of thefirst value 123456 and the calculated differences −11111, −11111, −2469,−11111, 1358, 1111, 1111, −1222, and −13.

The first compressor 310 may generate second new data by comparing eachof the values of the first new data with its preceding value to detect avalue whose sign is changed (that is, the second value −1111, the sixthvalue 1358, and the ninth value −1222), showing the signs of thedetected values (that is, the second value −1111, the sixth value 1358,and the ninth value −1222), and removing signs of the remaining values.

In this case, as shown in Table 8, by considering that the first value123456 of the first new data has no preceding value and thus nocomparison target, the sign of the first value 123456 of the second newdata may also be shown.

According to another exemplary embodiment, the first compressor 310 maycalculate differences between reference continuous data that ispreviously acquired and the original continuous data and generate newdata on the basis of the calculated differences.

As described above, the absorbance data has similar values even when theabsorbance data is measured at a different time in regards to the samesample. Accordingly, if the original continuous data has similar valueseven when the original continuous data is measured at a different timelike the absorbance data, it is possible to reduce the amount of data bydetermining the previously acquired continuous data as a criteria andgenerating new data on the basis of differences with the determinedcriteria.

TABLE 9 REFERENCE 123556 113345 101334 99765 88654 90012 90523CONTINUOUS 91034 89012 79999 DATA ORIGINAL 123456 112345 101234 9876587654 89012 90123 CONTINUOUS 91234 90012 89999 DATA NEW −100 −1000 −100−1000 −1000 −1000 −400 200 DATA 1000 10000

Referring to Table 9, for example, the first compressor 310 maycalculate differences between the reference continuous data and theoriginal continuous data and generate new data composed of thecalculated differences −100, −1000, −100, −1000, −1000, −1000, −400,200, 1000, and 10000.

The above-described various methods for reducing the amount of data maybe applied individually or in combination.

The second compressor 320 may apply various lossless compressionalgorithms or lossy compression algorithms to the generated new data tocompress the new data. Here, the lossless compression algorithms mayinclude an RLE algorithm, a dictionary-based encoding algorithm (e.g., aLempel-Ziv algorithm), a Huffman coding algorithm, etc., and the lossycompression algorithms may include an MPEG algorithm, a JPEG algorithm,etc., but are not limited thereto.

FIG. 4 is a block diagram showing a first compression unit according toan embodiment. A first compressor 310 a is an example of the firstcompressor 310 of FIG. 1.

Referring to FIG. 4, the first compressor 310 a may include a new-datagenerator 410, a sign remover 420, and a decimal point remover 430.

The new-data generator 410 may calculate differences between adjacentvalues in the original continuous data and generate new data on thebasis of the calculated differences. For example, the new-data generator410 may calculate the differences between the adjacent values in theoriginal continuous data and generate new data composed of the firstvalue of the original continuous data and the calculated differences.

As described above, because the absorbance data changes gradually withthe change in wavelength, an absorbance value at a specific wavelengthhas no significant difference from that at its preceding wavelength.Accordingly, if the original continuous data changes gradually like theabsorbance data, it is possible to reduce the amount of data bygenerating new data on the basis of the difference between the adjacentvalues of the original continuous data.

The sign remover 420 may compare each of a plurality of values of thenew data generated by the new-data generator 410 with its precedingvalue to detect a value whose sign is changed, show the sign of thedetected value, and remove the signs of the remaining values. Inaddition, by considering that the first value of the new data has nopreceding value and thus no comparison target, the sign of the firstvalue of the new data may also be shown.

As described above, because the absorbance data changes gradually withthe change in wavelength, an absorbance value at a specific wavelengthhas no significant difference from that at its preceding wavelength.Accordingly, if the original continuous data changes gradually, like theabsorbance data, the differences between the adjacent values haveconsecutive negative signs (−) or positive signs (+). Accordingly, ifthere are consecutive negative signs or positive signs, it is possibleto further reduce the amount of data by showing only the first sign ofthe consecutive signs.

If the values of the generated new data have the same number of decimalplaces, the decimal point remover 430 may remove a decimal point fromeach of the values of the new data. In addition, the decimal pointremover 430 may remove a zero before a significant digit from the valuesof the new data.

If the original continuous data is expressed as values with the samenumber of decimal places, the new data generated on the basis of thedifferences between the adjacent values of the original continuous datamay also be expressed as values with the same number of decimal placesas the original continuous data. Accordingly, it is possible to reducethe amount of new data by deleting a decimal point and a zero before asignificant digit from each of the values of the new data to convert thevalues into integers.

FIG. 5 is a block diagram showing a first compressor according toanother exemplary embodiment. A first compressor 310 b is an example ofthe first compressor 310 of FIG. 3.

Comparing FIG. 4 and FIG. 5, the first compressor 310 b of FIG. 5 may beconfigured to perform a decimal point removal function before a new datageneration function.

In other words, the decimal point remover 430 may remove a decimal pointand a zero before a significant figure from each of the values of theoriginal continuous data, and the new-data generator 410 may calculatedifferences between adjacent values in the original continuous data fromwhich the decimal points and the zeros before the significant figureshave been removed, and generate new data on the basis of the calculateddifferences.

FIG. 6 is a flowchart showing a method of compressing continuous dataaccording to an exemplary embodiment.

Referring to FIGS. 3 and 6, the compression device 210 may reduce theamount of original continuous data to generate new data (operationS610).

According to an exemplary embodiment, the compression device 210 mayselect, from the original continuous data, values of meaningful partsthat are important in analyzing the original continuous data andgenerate new data having a smaller amount than the original continuousdata on the basis of the selected values.

According to another exemplary embodiment, the compression device 210may round off the values of the original continuous data to apredetermined number of decimal places and generate new data having asmaller amount than the original continuous data on the basis of aresult of the rounding-off.

According to another exemplary embodiment, the compression device 210may select some values of the original continuous data using amulti-variable analysis technique such as PCA and generate new datahaving a smaller amount than the original continuous data on the basisof the selected values.

According to another exemplary embodiment, the compression device 210may select values from the original continuous data at a predetermineddata interval and generate new data having a smaller amount than theoriginal continuous data on the basis of the selected values.

In this case, non-selected values may be predicted using a predefinedprediction equation on the basis of values of new data. The predictionequation may be defined as an average of multiple adjacent values andmay also be defined as a function for the multiple adjacent values(e.g., a polynomial).

If the method of reducing the amount of data on the basis of valuesselected at a predetermined data interval is used, the compressiondevice 210 may find a difference between a predicted value and anoriginal value and generate prediction difference data on the basis ofthe difference. The prediction difference data may be used to restorethe original data without any loss.

According to another exemplary embodiment, if the values of the originalcontinuous data have the same number of decimal places, the compressiondevice 210 may convert the values of the original continuous data intointegers and generate new data having a smaller amount than the originalcontinuous data on the basis of a result of the conversion. For example,it is possible to reduce the amount of original continuous data bydeleting a decimal point and a zero before a significant digit from eachof the values of the original continuous data to convert the values intointegers.

According to another exemplary embodiment, the compression device 210may calculate differences between adjacent values of the originalcontinuous data and generate new data having a smaller amount than theoriginal continuous data on the basis of the calculated differences.

According to another exemplary embodiment, the compression device 210may reduce the amount of data by generating new data on the basis of thedifferences between the adjacent values of the original continuous data,comparing each of a plurality of values of the generated new data withits preceding value to detect a value whose sign is changed, showing thesign of the detected value, and removing signs of the remaining values.

According to another exemplary embodiment, the compression device 210may calculate differences between reference continuous data that ispreviously acquired and the original continuous data, and generate newdata on the basis of the calculated differences.

The above-described various methods for reducing the amount of data maybe applied individually or in combination.

The compression device 210 may apply various lossless compressionalgorithms or lossy compression algorithms to the generated new data tocompress the new data (operation S620). Here, the lossless compressionalgorithms may include an RLE algorithm, a dictionary-based encodingalgorithm (e.g., a Lempel-Ziv algorithm), a Huffman coding algorithm,etc., and the lossy compression algorithms may include an MPEGalgorithm, a JPEG algorithm, etc., but are not limited thereto.

FIG. 7 is a flowchart showing a method of reducing the amount oforiginal continuous data according to an exemplary embodiment.

Referring to FIGS. 4 and 7, the first compressor 310 a may calculatedifferences between adjacent values in original continuous data andgenerate new data on the basis of the calculated differences (operationS710). For example, the first compressor 310 a may calculate differencesbetween adjacent values in the original continuous data and generate newdata composed of a first value of the original continuous data and thecalculated differences.

The first compressor 310 a may compare each of a plurality of values ofthe generated new data with its preceding value to detect a value whosesign is changed, show the sign of the detected value in the new data,and remove signs of the remaining values (operation S720). Here, byconsidering that the first value of the new data has no preceding valueand thus no comparison target, the sign of the first value of the newdata may also be shown.

If the values of the generated new data have the same number of decimalplaces, the first compressor 310 a may remove a decimal point and a zerobefore a significant digit from each of the values of the new data(operation S730).

While not restricted thereto, an exemplary embodiment can be embodied ascomputer-readable codes in a computer-readable recording medium thatincludes program instructions to be implemented by a computer to cause aprocessor to execute or perform the program instructions. The medium mayalso include, alone or in combination with the program instructions,data files, data structures, and the like. The computer-readablerecording medium may include any kind of recording devices for storingdata which can be thereafter read by a computer system. Examples of thecomputer-readable recording medium include a ROM, a RAM, a compact discROM (CD-ROM), a magnetic tape, a floppy disk, an optical disc, and soon. The described hardware devices may be configured to act as one ormore software modules in order to perform the operations and methodsdescribed above, or vice versa. In addition, the computer-readablerecording medium may be distributed over network-coupled computersystems so that the computer-readable code is stored and executed in adistributed fashion.

Also, an exemplary embodiment may be written as a computer programtransmitted over a computer-readable transmission medium, such as acarrier wave, and received and implemented in general-use orspecial-purpose digital computers that execute the programs. Moreover,it is understood that in exemplary embodiments, one or more units of theabove-described apparatuses and devices can include circuitry, aprocessor, a microprocessor, etc., and may execute a computer programstored in a computer-readable medium.

While one or more exemplary embodiments have been described withreference to the figures, it should be understood by those skilled inthe art that various changes in form and details may be made thereinwithout departing from the spirit and scope as defined by the appendedclaims. Therefore, the description of the exemplary embodiments shouldbe construed in a descriptive sense only and not to limit the scope ofthe claims, and many alternatives, modifications, and variations will beapparent to those skilled in the art.

What is claimed is:
 1. An apparatus for compressing continuous data, theapparatus comprising: a first compressor comprising: a data generatorconfigured to calculate differences between adjacent values in originalcontinuous data and generate data based on the calculated differences;and a sign remover configured to compare each of a plurality of valuesof the generated data with its preceding value to detect a value whosesign is changed, show a sign of a detected value, and remove signs ofremaining values.
 2. The apparatus of claim 1, wherein the originalcontinuous data is Photoplethysmograph (PPG) data or absorbance data. 3.The apparatus of claim 1, wherein the generated data comprises a firstvalue of the original continuous data and the calculated differences. 4.The apparatus of claim 1, wherein the sign remover is configured to showa sign of a first value of the generated data.
 5. The apparatus of claim1, further comprising a decimal point remover configured to remove adecimal point from each values of the generated data if the generateddata comprises values with an identical number of decimal places.
 6. Theapparatus of claim 5, wherein the decimal point remover is configured toremove a zero before a significant digit from the values of thegenerated data.
 7. The apparatus of claim 1, further comprising a secondcompressor configured to compress the generated data from which thesigns of the remaining values are removed using a lossless compressionalgorithm.
 8. The apparatus of claim 7, wherein the lossless compressionalgorithm is one of a run-length encoding (RLE) algorithm, adictionary-based encoding algorithm, and a Huffman coding algorithm. 9.A method of compressing continuous data, the method comprising:calculating, by a first compressor, differences between adjacent valuesin original continuous data; generating, by the first compressor, databased on the calculated differences; and comparing, by the firstcompressor, each of a plurality of values of the generated data with itspreceding value to detect a value whose sign is changed, showing a signof a detected value, and removing signs of remaining values.
 10. Themethod of claim 9, wherein the original continuous data isPhotoplethysmograph (PPG) data or absorbance data.
 11. The method ofclaim 9, wherein the generated data comprises a first value of theoriginal continuous data and the calculated differences.
 12. The methodof claim 9, further comprising showing, by the first compressor, a signof a first value of the generated data.
 13. The method of claim 9,further comprising removing, by the first compressor, a decimal pointfrom each values of the generated data if the generated data comprisesvalues with an identical number of decimal places.
 14. The method ofclaim 13, further comprising removing, by the first compressor, a zerobefore a significant digit from each of the values of the generateddata.
 15. The method of claim 9, further comprising compressing thegenerated data from which the signs of the remaining values are removedusing a lossless compression algorithm.
 16. The method of claim 15,wherein the lossless compression algorithm is one of a run-lengthencoding (RLE) algorithm, a dictionary-based encoding algorithm, and aHuffman coding algorithm.